- Original research
- Open Access
Digital soil mapping for fire prediction and management in rangelands
© The Author(s). 2018
- Received: 15 October 2018
- Accepted: 25 October 2018
- Published: 27 December 2018
Soil properties have important effects on fire occurrence and spread, but soils are often overlooked in fire prediction models. Quantifying soil−fire linkages is limited by information in conventional soil maps, but digital soil mapping products (e.g., detailed soil property maps) could improve both wildfire prediction models and post-fire management decisions.
Of our estimated 3.7 Mkm2 of rangeland in the continental US and Alaska, an average of 38 000 km2 burned per year between 2008 and 2017. To highlight the role of soils in fire ecology, we present 1) a conceptual framework explaining why soil information can be useful for fire models, 2) a comprehensive suite of literature examples that used soil property information in traditional soil survey for predicting wildfire, and 3) specific examples of how more detailed soil information can be applied for pre- and post-fire decisions.
Digital soil mapping can improve fire prediction models and inform post-fire management decisions.
Las propiedades del suelo tienen efectos importantes en la ocurrencia y propagación de incendios, aunque los suelos son frecuentemente pasados por alto en los modelos de predicción de incendios. La cuantificación de los vínculos entre suelo y fuegos está limitada por la información contenida en mapas de suelos convencionales, aunque los productos de mapas de suelo digitales (i.e., mapas detallados de propiedades del suelo) pueden mejorar tanto la predicción de incendios como las decisiones de manejo post-fuego.
De nuestras estimaciones de 3,7 Mkm2 de pastizales naturales en la parte continental de EEUU y Alaska, un promedio de 38 000 km2 se quemaron por año entre 2008 y 2017. Para resaltar el rol de los suelos en la ecología del fuego, presentamos 1) un marco conceptual explicando por qué la información sobre el suelo puede ser útil para modelos de incendios, 2) un conjunto comprensivo de ejemplos de la literatura que usan información sobre las propiedades del suelo en relevamientos de suelo tradicionales para predecir incendios, y 3) ejemplos específicos de cómo una información de suelos más detallada puede aplicarse para tomar decisiones pre- y post- fuegos.
Los mapas de suelo digitales pueden mejorar los modelos de predicción de incendios e informar sobre decisiones de manejo post-fuego.
- digital soil mapping
- fire effects
- soil moisture
- spatial modeling
DSM: Digital Soil Map
GRACE: NASA’s Gravity Recovery and Climate Experiment
gSSURGO: gridded SSURGO
HWSD: Harmonized World Soil Database
MTBS: Monitoring Trends in Burn Severity database
NASA: National Aeronautics and Space Administration
NRCS: USDA Natural Resources Conservation Service
SMAP: NASA’s Soil Moisture Active Passive mission
SMOS: European Space Agency’s Soil Moisture and Ocean Salinity mission
SSURGO: Soil Survey Geographic Database
STATSGO2: Digital General Soil Map of the United States
In the western US, there is considerable concern regarding the increased negative effects of fire on plant invasions and erosion (see 2011 special issue of Rangeland Ecology and Management 64: 429–478). Modified fire regimes resulting from the proliferation of invasive species can lead to increased fire likelihood, putting more landscapes at risk of soil erosion (Brooks 2006). Fire effects on soil properties are strongly influenced by burn severity, which often varies significantly in space (Moody et al. 2013). Interestingly, burn severity does not always align with the fuel load (Stoof et al. 2013). This can create complex patterns of site susceptibility to hydrophobicity, erosion, and subsequent hydrologic responses (Williams et al. 2014)
Multiscale processes control fire occurrence and long-term fire regimes (Allen 2007; Falk et al. 2011). The most common variables used to model and predict fire occurrence are derived from topography, precipitation, and vegetation condition because of their relationships with fuel conditions (Littell et al. 2009; Abatzoglou 2013). These are often complemented by other properties related to fire ignition such as distance to road and lightning strike density (Yang et al. 2015). Most variables included in fire prediction models attempt to represent the necessary elements of fire occurrence: available fuel, favorable conditions for burning, and some ignition source (Krawchuk 2011).
Soil properties are frequently absent from fire prediction models (Brooks 2006; Littell et al. 2009; Hawbaker et al. 2013; Gray and Dickson 2014), although some recent studies have begun to utilize soil information such as soil moisture to predict fire occurrence. For example, Krueger et al. (2015) found that measured soil moisture strongly influences wildfire activity during much of the year in Oklahoma, USA, because it influences plant productivity and live fuel moisture directly. Soil moisture has also been shown to be a better predictor of the occurrence of large growing-season wildfires than the commonly used Keetch-Byram drought index (Krueger et al. 2017). Remotely sensed soil moisture has also been used recently to predict wildfire occurrence across the contiguous US (Jensen et al. 2018). Integrating bottom-up (e.g., soil, topography) and top-down (e.g., precipitation, temperature) controls of wildfire is necessary for refining local models of fire susceptibility and improving our ability to produce fire risk assessments in a rapidly changing climate.
In addition to fire prediction models, soil properties are especially important for managing soil landscapes post fire because fire behavior directly influences soil conditions that interact with flora, fauna, and landscapes to impact processes such as runoff and erosion (Hyde et al. 2013). In contrast to wildfire predictions, soil information is commonly included in post-fire management and modeling. Even though soil is recognized as an important element of post-fire management, there is still an imperative need to better quantify the interactions between fire severity and hydraulic soil properties across a wide range of spatial scales (Moody et al. 2013). Many wildland fires happen in remote areas where on-the-ground inventories of soil, vegetation, and burn severity have been inhibited by cost, time, or logistics. Optimizing available resources for pre-and post-fire applications requires the integration of a comprehensive suite of environmental data.
Fire is a natural process in many rangeland systems, and being able to predict when and where on the landscape it will occur continues to be a critical need (Rangeland Fire Task Force 2015). Post-fire management decisions also become more important as larger areas experience fire, putting more areas at risk for soil erosion and subsequent degradation and water quality issues (Fig. 2). We believe that both pre- and post-fire management decisions could benefit from more applied uses of existing and newly generated soil maps. The goals of this paper are to illustrate the importance of including soil property information in fire prediction models and post-fire response, describe map-based soil information that is currently available, and discuss the potential for digital soil mapping to improve pre- and post-fire management decisions in rangelands.
Soil−vegetation relationships and subsequent fire distribution vary with climate. This can be discussed in the context of a resource-limited system compared to one with ample biomass (Krawchuk 2011). For example, in semiarid rangelands, soil moisture exerts strong control on site characteristics such as vegetation community structure as well as current conditions of fire susceptibility like fuel load and moisture status. In very cold rangeland systems where permafrost controls soil drainage (e.g., Alaskan tundra), temperature can influence effective soil depth and subsequent soil moisture conditions. Spatial and temporal soil moisture conditions are also affected by a suite of soil properties including texture, rock fragments, and organic matter. Therefore, long-term projections of fire likelihood can be tied to soil types or key properties affecting soil moisture. The interaction of soil with climate is an important element of both short- and long-term projections of fire.
Spatial soil information currently available for modeling fire in the United States. 1SS = soil survey; DSM = digital soil map; PRS = proxy remote sensing for soil properties. 2Both represents both raster and vector formats available. 31:250 000 in continental US and 1:1 000 000 in Alaska
Advantage for pre or post fire
Disadvantage for pre or post fire
Method of development
1:1 000 000 to 1:5 000 000
Extensive spatial coverage
Coarse resolution; limited interpretations
Merged European Soil Database, soil map of China, regional SOTER databases, and Soil Map of the World
Wieder et al. 2014
1:250 000 to 1:1 000 0003
Extensive spatial coverage
Coarse resolution; limited interpretations
Soil-landscape paradigm; tacit knowledge; field and laboratory sampling and analysis
Soil Survey Staff 2018c
SSURGO and gSSURGO
1:12 000 to 1:63 360
Extensive spatial coverage; numerous interpretations and properties
Variability within map units; some areas without data
250 m and 1 km raster cells
Extensive spatial coverage; quantified model uncertainty; data gaps filled
Varying sample density in each soil type
Probabilistic based machine learning
Hengl et al. 2017
100 m raster cells
Ramcharan et al. 2018
30 m raster cells
Chaney et al. 2016
Local DSM maps
Regional and local
Variable (as detailed as 5 m raster cells)
Fine spatial resolution; some data gaps filled; quantified model uncertainty
Varying sample density in each soil type; difficult to locate; often specific goals
Many methods ranging from regression to machine learning
Grunwald 2009; many others
Direct soil moisture (e.g., SMAP, SMOS, GRACE)
3 to 36 km raster cells
Near current data availability
Limited data record; Coarse spatial scale
Satellite remote sensing with ground validation
Indirect soil moisture
Regional and local
Quantitative and process-based information
Relies on empirical relationships and subject to model uncertainty
E.g., vegetation indices, inverse process-based models, land surface models
Conventional soil mapping
Conventional soil maps provide information about soil properties that relate to soil genesis, morphology, and classification using polygons for spatial representation (Soil Science Division Staff 2017). The mapping scale is generally dependent upon the specific management goals of the project. Soil data available are usually representative values of named soil types, which are aggregated for spatial representation in polygons. This results in soil map units that can have coarse representation of soil property variability within the polygon. A variety of conventional soil maps have been compiled as the Harmonized World Soil Database (HWSD; Table 1) to provide global coverage (Wieder et al. 2014). These polygon-based maps of global extent generally have coarse spatial resolution and somewhat generalized attribute information due to limitations of mapping such large areas. Many regional soil maps cover individual countries at various resolution and detail. In the US, there are three main soil products including the Digital General Soil Map of the United States (STATSGO2; Soil Survey Staff 2018c), Soil Survey Geographic Database (SSURGO; Soil Survey Staff 2018b), and gridded SSURGO (gSSURGO; Soil Survey Staff 2018a) (Table 1). These provide more detailed information than global datasets, with estimated soil properties and interpretations for land management (Soil Science Division Staff 2017). While these maps are produced by expert soil scientists, there are limitations on the quantification of soil property variability and spatial distribution across the landscape.
Some studies have used static soil properties from conventional soil maps to model wildfires. Levi (2016) found soil water holding capacity and ecological sites to be useful for explaining fire occurrence in desert grasslands of the southwestern US. Dilts and Sibold (2009) explored the use of soil water holding capacity and infiltration rate to model fire, but found insignificant effects and removed the variable from subsequent models, likely a reflection of the coarse-scaled soil information that they derived from the STATSGO2 database. Coarse resolution of soil inputs have also been identified as a limitation for fire prediction models in northern Wisconsin, where Sturtevant (2007) used STATSGO2 to derive soil water holding capacity, drainage class, and hydric soil ratings. Harden et al. (2001) used a simple metric of soil drainage integrated from soil water holding capacity, infiltration rate, and hydraulic conductivity to characterize the state of Alaska, and then related that to wildfire. They determined that more poorly drained areas had more fire activity than better drained areas. However, they used the STATSGO2 database, and soil map units were about six times larger than the fire polygons, which only allowed for relatively simple statistical analyses. We suggest that using soil information that is better matched to the scale of interest for a particular fire modeling application could be more informative. For example, the above studies may have had better relationships between fire predictions and soil properties if they had used more detailed SSURGO data or a suite of digital soil mapping products.
Digital soil mapping
Digital soil mapping is an approach for predicting soil properties or soil types by incorporating measured soil properties at known point locations with environmental covariate layers having continuous spatial coverage (e.g., Landsat satellite data, digital elevation models; McBratney and Santos 2003, Scull et al. 2003). These soil prediction models can utilize simple regression or complex machine learning and generally provide improved estimates of soil properties at a finer spatial scale than currently available soil map products. A tremendous benefit of digital soil mapping models is the ability to produce some measure of model accuracy or confidence that can be incorporated into subsequent models. Digital soil mapping is generally used to predict static soil properties, but these properties can be incorporated with other models to derive more dynamic soil properties.
Digital soil mapping is a practical solution for refining the spatial variability of soil information for large areas. A variety of digital soil maps are currently available including global (SoilGrids; Hengl et al. 2017) and regional (e.g., POLARIS; Chaney et al. 2016) products (Table 1). SoilGrids is a global product of soil property information available in a raster data format with 250 m resolution (Hengl et al. 2017), with recent advances for the continental US that provide data for 100 m pixels (Ramcharan et al. 2018). There are also local scale examples of digital soil mapping that could be useful for fire modeling. For example, the 2009 Hog Fire burned 73 km2 in southeastern Arizona, USA, where approximately half of the burned area occurred on national forest land with no published soil survey available (Fig. 4). However, a digital soil map is available from work by Levi (2017) and provides predicted soil components for most of the burned area. A simple intersection of the digital soil map and the Hog Fire boundary indicated that two soil components accounted for 79% of the burned area. One drawback of digital soil mapping data for fire modeling is that knowing about and then accessing localized data may be difficult as there is not currently a clearinghouse or repository of these data. Some review papers offer one mechanism of identifying existing studies (e.g., Grunwald 2009) and the USDA Natural Resources Conservation Service (NRCS) has compiled an annotated bibliography of digital soil mapping projects with NRCS participation (see NRCS 2018), but such lists are often not comprehensive. Some larger-scale projects like the 100 m SoilGrids project are readily available (Table 1). Another challenge to utilizing previously developed digital soil maps is that project objectives may have produced soil property maps that would not be easily translated to relevant fire ecology questions.
Soil moisture mapping
Perhaps the greatest potential for better incorporating soil information into fire modeling is through soil moisture. Soil moisture conditions are tied closely to live fuel moisture content, which is a critical element of wildfire risk models (Qi et al. 2012). The spatial coverage of in situ soil moisture measurements represents only a very small fraction of the landscapes on which wildfires most commonly occur. Efforts to compile these measured data, such as the North American Soil Moisture Database (Quiring et al. 2016), present more opportunities for utilizing soil moisture measurements in fire research. Advancements in soil moisture modeling also provide much needed information for incorporating into an assortment of applications, including fire modeling. A variety of methods exist to predict soil moisture for large spatial areas including cosmic-ray neutron radiation, indirect Global Positioning System signals, remotely sensed land surface temperature measurements, and remote sensing missions specifically designed to measure soil moisture (Ochsner et al. 2013). In recent years, remotely sensed soil moisture data has been used to model fire activity for large areas using several platforms including the European Space Agency’s Soil Moisture and Ocean Salinity mission (SMOS; Chaparro et al. 2016) and National Aeronautics and Space Administration’s (NASA) Gravity Recovery and Climate Experiment (GRACE; Jensen et al. 2018). NASA’s Soil Moisture Active Passive (SMAP) mission also has tremendous potential for providing valuable soil moisture datasets that could be applied to fire prediction models (Entekhabi et al. 2010). Remote sensing missions may offer the greatest potential to inform pre- and post-fire applications for large areas; however, the coarse spatial resolution remains a limitation for landscape scales (Jensen et al. 2018).
In lieu of directly mapping soil moisture, another approach is to use models for predicting soil moisture conditions. For example, Abatzoglou (2013) used a land surface model to derive soil moisture for predicting area burned across the western US. Coops and Waring (2012) derived soil fertility and available soil water holding capacity for forested areas in a large area of western North America by inverting a forest growth model adjusted with remotely sensed leaf area index. Krawchuk (2011) used modeled soil moisture to explore the influence of global resource gradients on fire distribution, and Waring (2016) used soil water balance to model large wildfires across the western US. These proxy measurements of soil properties can be useful for interpreting factors such as soil moisture, leaf area index, and fuel moisture using remote sensing that can also aid in prediction of pre- and post-fire processes.
Fire danger systems
The most immediate benefit of having detailed soil information prior to fire occurrence is the potential for refining fire prediction models of occurrence and burn severity. Improved soil information with spatially explicit estimates of model confidence can allow utilization of more quantitative fire−soil relationships in fire danger systems. Soil moisture models that require physical soil properties for accurate representation of spatiotemporal soil moisture conditions will also benefit the fire modeling community by providing more robust inputs for dynamic fire risk assessment.
Creative applications of these data can facilitate the development of new prediction tools. For example, derivatives of soil moisture related to the fraction of available soil water can be more useful for predicting fire occurrence than actual soil moisture (Krueger et al. 2015, Waring 2016). Incorporating antecedent conditions of soil moisture (Krawchuk 2011) can be refined with more detailed soil property information resulting from digital soil mapping. Applying these drought index concepts specifically to soil moisture conditions and how that relates to the potential of the soil to hold water requires better constraints on soil properties than currently available with conventional soil maps.
Interpreting complex relationships between soil, vegetation, climate, and management, and the subsequent feedbacks with fire requires spatially explicit information. As we continue to refine our understanding of fire-prone environments and predict the impacts of changing climate and management, there is an increasing need to quantify all factors involved. Conventional soil maps provide valuable information; however, the scale of soil mapping in many forest and rangeland landscapes limits our ability to derive site-specific relationships necessary for advancing the science of fire ecology. Digital soil mapping techniques present an opportunity to better quantify the relationships between soil and fire, which has largely been unexplored.
Digital soil maps can potentially unlock interdisciplinary scientific questions related to fire ecology. For example, a recent study in Alaska used digital soil mapping to predict soil moisture and interpret fire severity. The authors estimated that 90% of the high-severity fire zone lacked permafrost after fire (Brown et al. 2016). Recent changes in the climate of northern latitudes have heightened concern regarding the melting of permafrost and subsequent effects on carbon dynamics and wildfire susceptibility; soil properties play a major role in these processes. A second example of applying digital soil maps could quantify the restoration trajectories of burned areas or design research studies to further investigate fire effects. For example, Nauman and Duniway (2016) developed a detailed soil map of particle size in the soil profile to identify matching soil-geomorphic sites on the Colorado Plateau, USA. They later combined the soil prediction map with other remote sensing data to evaluate the ecological recovery of disturbed sites following oil and gas extraction (Nauman et al. 2017). This same process could be applied to identify similar soil-geomorphic zones for monitoring and comparing burned and unburned areas.
We know that soils interact with climate, vegetation, and management to control the trajectory of post-fire recovery rates for a given landscape. The first-order effects of fire on soil are related to the changes that happen when soil is heated (Massman and Frank 2010), and the degree of alteration for different soil properties is directly related to soil temperatures reached during a fire (Alcaniz et al. 2018); thus, conditions at the time of fire determine the effect of a fire on the soil. Soil heating is also a major factor controlling the recovery of plants following fire because of the effects on existing vegetation and seed bank (Stephan and Miller 2010; Smith and Abella 2014). Dynamic soil properties like soil moisture and organic matter content interact with static soil properties (e.g., texture, pore space) resulting in varying degrees of heat transfer and soil alteration that vary across spatial and temporal scales (Moody et al. 2013). Detailed knowledge of soil conditions, such as those obtained from digital soil mapping, can enable more quantitative interpretations of soil−fire interactions than are currently available with existing soil information.
Soil erosion models
It is well accepted that fire can adversely affect surface soil properties and alter the spatial patterns of soil resources (Allen and Steers 2011, Sankey et al. 2012a, Sankey et al. 2012b). This is important for predicting erosion and revegetation, but conducting post-fire surveys of soil conditions are expensive and often challenging to complete in a timely manner. It is typical to utilize soil surveys to obtain properties related to erosion and revegetation potential and other characteristics (US National Park Service 2006). Soil maps are thus a critical component for post-fire planning and assessment. For example, soil burn severity assessments are commonly linked with existing models that predict post-fire hydrology and erosion using soil property information (Parsons et al. 2010). Numerous models have been used to predict erosion and debris flow following fire including WEPP (Laflen et al. 1997), GeoWEPP (Renschler 2003), ERMiT (Robichaud et al. 2007), and Ravel RAT (Fu 2004), all of which require soil property inputs (Miller et al. 2016). In most cases, conventional soil maps like STATSGO2 and SSURGO are used to derive these inputs; however, digital soil maps can provide more detailed information with spatially explicit representations of model confidence that can subsequently be incorporated into landscape models.
Digital soil mapping can also provide soil information in areas for which conventional soil maps are currently unavailable (e.g., national forests; Fig. 4). One of the largest areas in the US lacking detailed soil survey information (i.e., SSURGO) is Alaska, which has approximately 801 000 km2 of rangeland (Fig. 1). Better soils information means better potential to model soil erosion and watershed effects following fire. Understanding the relationships between burn severity and soil properties is an exceptionally high priority for post-wildfire runoff and erosion research (Moody et al. 2013). There is a great need to have a quantitative data set of important soil information (among other data) to facilitate rapid modeling in response to fire (Miller et al. 2016).
Advances in soil modeling offer solutions for resolving the scarcity of relevant soil property information necessary for improving fire modeling. Observed trends in the burned area of US rangelands underscore the need to improve fire danger systems in these areas. There are clear contributions of soil properties to fire occurrence that are not fully being utilized by the fire modeling community. Soil properties are commonly assessed and used to predict erosion and landscape recovery after fire because they strongly influence these responses in burned areas. We believe that soil maps and other soil property information have the potential to advance our ability to predict fire likelihood and model watershed-scale processes for areas after they burn. Digital soil mapping presents an opportunity to advance our understanding of soil−fire relationships by providing detailed soil information necessary for site-specific interpretations. Applying more quantitative soil information to fire science will provide more tools for both pre- and post-fire management decisions, which translate to more effective and efficient use of resources for mitigating negative effects in burned areas.
We would like to acknowledge the comments of two anonymous reviewers who provided valuable comments for the final version of this manuscript.
This work was supported by the USDA ARS Postdoctoral Research Associate Program.
Availability of data and materials
The datasets generated or analyzed during this study are available in the following repositories: Monitoring Trends in Burn Severity repository http://mtbs.gov/direct-download, the National Land Cover Dataset repository https://www.mrlc.gov/nlcd2011.php, and the Jornada Spatial Data Catalog https://jornada.nmsu.edu/data-catalogs/spatial, or otherwise available from the corresponding author on reasonable request.
MRL initiated analysis, processed data, performed data analysis, and created figures. BTB guided analyses and overarching focus. Both authors wrote the paper, interpreted data, developed figures, edited the manuscript, and have given final approval of the version to be published.
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